1. Field
The embodiments relate to the field of image processing, and particularly, to a gray image processing method and apparatus.
2. Description of the Related Art
Currently, with the popularization and development of the electronic devices such as digital camera and scanner, the digital images can be easily obtained. However, any physical sensor (e.g., thermal sensor, electric sensor, or other type of sensor) will be influenced by noises at certain extent, and those noises influence the signal measurement authenticity. As a result, any image data obtained includes signals and noises. Various image-related applications, such as medical image analysis, image segmentation and object detection, usually require an effective noise suppression method to further obtain a reliable result. Thus, the image filtration has become one of the most important and extensive research topics about image processing and computer vision. The gray image is one type of image significant and widely used, so its noise suppression method is very important.
In the prior art, in order to denoise the image, many methods such as wavelet threshold method, non-local means method, Gaussian filtering method, and bilateral filtering method have been tried to filter the gray image.
In which, the wavelet threshold method applies a wavelet transform to the original image to transform it into the wavelet domain, and filters multi-channel wavelet coefficients by using a threshold method, wherein the wavelet coefficients usually include a slash detail coefficient, a horizontal detail coefficient and a vertical detail coefficient in the decomposition of level 1. At present, a known threshold method is the hard threshold, which sets all the detail coefficients within a range from zero to a set value, and finally, all the wavelet coefficients after the threshold setting are returned to the image domain through an inverse wavelet transform. This method can suppress noises, but some image details will also be suppressed.
The non-local means method is a non-linear edge reservation filtering method, which calculates a weighted sum of the input pixels as each output pixel. Due to the collection of the input pixels, one output pixel may originate from a large area of the input image, and then become “non-local”. One key feature of the non-local means method is that the weighted value is determined by the distance between the small image blocks. The method can reserve the image details and effectively suppress the Gaussian noise. However, in some practical applications, other noises besides the Gaussian noise are also existed, and they cannot be effectively removed by using this method.
The Gaussian filtering method is a weighted means method and each output pixel is set as a weighted means of around pixels, wherein the weighted value of the original pixel is the maximum, and the weighted values of around pixels gradually decrease with the increase of the distance away from the original pixel. The method can reduce the noises by smoothing the image, but the image details are also degraded.
The bilateral filtering method is a smooth filtering method of edge reservation and noise reduction, and the brightness value of each pixel in the image is replaced by the weighted means of the brightness values of adjacent pixels. The method is based on Gaussian distribution, and the key is that those weighted values are based on not only the Euclidean distance, but also the radiation difference. The method can reserve the sharp edges through each traverse pixel of the system and corresponding weighted values of adjacent pixels. However, if the image has high noises, the method will distort the image edges.
It is clear that none the existing methods can well reserve the image details while effectively removing various image noises.
To be noted, the above introduction to the technical background is just made for the convenience of clearly and completely describing the technical solutions, and to facilitate the understanding of a person skilled in the art. It shall not be deemed that the above technical solutions are known to a person skilled in the art just because they have been illustrated in the Background section.